In recent years, great achievements have been made in graph convolutional network (GCN)
for non-Euclidean spatial data feature extraction, especially the skeleton-based feature …

Abstract Graph Convolutional Networks (GCNs) have been widely used in skeleton-based
action recognition with remarkable achievements. Many recent studies model the human …

Although graph convolutional networks (GCNs) have shown their demonstrated ability in
skeleton-based action recognition, both the spatial and the temporal connections rely too …

In recent years, great progress has been made in the recognition of skeletal behaviors
based on graph convolutional networks (GCNs). In most existing methods, however, the …

Sports posture recognition plays a crucial role in modern sports science and training.
Posture recognition and analysis plays a positive role in improving sports quality and …

In the realm of temporal localization in videos, our research introduces a novel framework
that achieves significant results in event localization in videos. We depart from conventional …

To improve the accuracy of human activity recognition (HAR) based on body area network
(BAN), a novel spatio-temporal network combining multi-channel convolutional neural …

Video event localization, the task of accurately identifying and localizing events within a
video, poses a significant challenge due to the complex nature of video data. The …

In the current light-weight CNN-based networks, convolutional operators are principally
utilized to extract feature maps for image representation. However, such conventional …

Different from other computer vision tasks, action recognition needs to process larger-scale
video data. How to extract and analyze the effective parts from a huge amount of video …